In [2]:
import numpy as np
import pandas as pd
df=pd.read_csv('mc donalds.csv')
In [3]:
df
Out[3]:
Unnamed: 0 Category Item Serving Size Calories Calories from Fat Total Fat Total Fat (% Daily Value) Saturated Fat Saturated Fat (% Daily Value) ... Carbohydrates Carbohydrates (% Daily Value) Dietary Fiber Dietary Fiber (% Daily Value) Sugars Protein Vitamin A (% Daily Value) Vitamin C (% Daily Value) Calcium (% Daily Value) Iron (% Daily Value)
0 0 Breakfast Egg McMuffin 4.8 oz (136 g) 300 120 13.0 20 5.0 25 ... 31 10 4 17 3 17 10 0 25 15
1 1 Breakfast Egg White Delight 4.8 oz (135 g) 250 70 8.0 12 3.0 15 ... 30 10 4 17 3 18 6 0 25 8
2 2 Breakfast Sausage McMuffin 3.9 oz (111 g) 370 200 23.0 35 8.0 42 ... 29 10 4 17 2 14 8 0 25 10
3 3 Breakfast Sausage McMuffin with Egg 5.7 oz (161 g) 450 250 28.0 43 10.0 52 ... 30 10 4 17 2 21 15 0 30 15
4 4 Breakfast Sausage McMuffin with Egg Whites 5.7 oz (161 g) 400 210 23.0 35 8.0 42 ... 30 10 4 17 2 21 6 0 25 10
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
255 255 Smoothies & Shakes McFlurry with Oreo Cookies (Small) 10.1 oz (285 g) 510 150 17.0 26 9.0 44 ... 80 27 1 4 64 12 15 0 40 8
256 256 Smoothies & Shakes McFlurry with Oreo Cookies (Medium) 13.4 oz (381 g) 690 200 23.0 35 12.0 58 ... 106 35 1 5 85 15 20 0 50 10
257 257 Smoothies & Shakes McFlurry with Oreo Cookies (Snack) 6.7 oz (190 g) 340 100 11.0 17 6.0 29 ... 53 18 1 2 43 8 10 0 25 6
258 258 Smoothies & Shakes McFlurry with Reese's Peanut Butter Cups (Medium) 14.2 oz (403 g) 810 290 32.0 50 15.0 76 ... 114 38 2 9 103 21 20 0 60 6
259 259 Smoothies & Shakes McFlurry with Reese's Peanut Butter Cups (Snack) 7.1 oz (202 g) 410 150 16.0 25 8.0 38 ... 57 19 1 5 51 10 10 0 30 4

260 rows × 25 columns

In [4]:
df.head()
Out[4]:
Unnamed: 0 Category Item Serving Size Calories Calories from Fat Total Fat Total Fat (% Daily Value) Saturated Fat Saturated Fat (% Daily Value) ... Carbohydrates Carbohydrates (% Daily Value) Dietary Fiber Dietary Fiber (% Daily Value) Sugars Protein Vitamin A (% Daily Value) Vitamin C (% Daily Value) Calcium (% Daily Value) Iron (% Daily Value)
0 0 Breakfast Egg McMuffin 4.8 oz (136 g) 300 120 13.0 20 5.0 25 ... 31 10 4 17 3 17 10 0 25 15
1 1 Breakfast Egg White Delight 4.8 oz (135 g) 250 70 8.0 12 3.0 15 ... 30 10 4 17 3 18 6 0 25 8
2 2 Breakfast Sausage McMuffin 3.9 oz (111 g) 370 200 23.0 35 8.0 42 ... 29 10 4 17 2 14 8 0 25 10
3 3 Breakfast Sausage McMuffin with Egg 5.7 oz (161 g) 450 250 28.0 43 10.0 52 ... 30 10 4 17 2 21 15 0 30 15
4 4 Breakfast Sausage McMuffin with Egg Whites 5.7 oz (161 g) 400 210 23.0 35 8.0 42 ... 30 10 4 17 2 21 6 0 25 10

5 rows × 25 columns

In [5]:
df.tail()
Out[5]:
Unnamed: 0 Category Item Serving Size Calories Calories from Fat Total Fat Total Fat (% Daily Value) Saturated Fat Saturated Fat (% Daily Value) ... Carbohydrates Carbohydrates (% Daily Value) Dietary Fiber Dietary Fiber (% Daily Value) Sugars Protein Vitamin A (% Daily Value) Vitamin C (% Daily Value) Calcium (% Daily Value) Iron (% Daily Value)
255 255 Smoothies & Shakes McFlurry with Oreo Cookies (Small) 10.1 oz (285 g) 510 150 17.0 26 9.0 44 ... 80 27 1 4 64 12 15 0 40 8
256 256 Smoothies & Shakes McFlurry with Oreo Cookies (Medium) 13.4 oz (381 g) 690 200 23.0 35 12.0 58 ... 106 35 1 5 85 15 20 0 50 10
257 257 Smoothies & Shakes McFlurry with Oreo Cookies (Snack) 6.7 oz (190 g) 340 100 11.0 17 6.0 29 ... 53 18 1 2 43 8 10 0 25 6
258 258 Smoothies & Shakes McFlurry with Reese's Peanut Butter Cups (Medium) 14.2 oz (403 g) 810 290 32.0 50 15.0 76 ... 114 38 2 9 103 21 20 0 60 6
259 259 Smoothies & Shakes McFlurry with Reese's Peanut Butter Cups (Snack) 7.1 oz (202 g) 410 150 16.0 25 8.0 38 ... 57 19 1 5 51 10 10 0 30 4

5 rows × 25 columns

In [6]:
df.isnull()
Out[6]:
Unnamed: 0 Category Item Serving Size Calories Calories from Fat Total Fat Total Fat (% Daily Value) Saturated Fat Saturated Fat (% Daily Value) ... Carbohydrates Carbohydrates (% Daily Value) Dietary Fiber Dietary Fiber (% Daily Value) Sugars Protein Vitamin A (% Daily Value) Vitamin C (% Daily Value) Calcium (% Daily Value) Iron (% Daily Value)
0 False False False False False False False False False False ... False False False False False False False False False False
1 False False False False False False False False False False ... False False False False False False False False False False
2 False False False False False False False False False False ... False False False False False False False False False False
3 False False False False False False False False False False ... False False False False False False False False False False
4 False False False False False False False False False False ... False False False False False False False False False False
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
255 False False False False False False False False False False ... False False False False False False False False False False
256 False False False False False False False False False False ... False False False False False False False False False False
257 False False False False False False False False False False ... False False False False False False False False False False
258 False False False False False False False False False False ... False False False False False False False False False False
259 False False False False False False False False False False ... False False False False False False False False False False

260 rows × 25 columns

In [7]:
df.isnull().sum()
Out[7]:
Unnamed: 0                       0
Category                         0
Item                             0
Serving Size                     0
Calories                         0
Calories from Fat                0
Total Fat                        0
Total Fat (% Daily Value)        0
Saturated Fat                    0
Saturated Fat (% Daily Value)    0
Trans Fat                        0
Cholesterol                      0
Cholesterol (% Daily Value)      0
Sodium                           0
Sodium (% Daily Value)           0
Carbohydrates                    0
Carbohydrates (% Daily Value)    0
Dietary Fiber                    0
Dietary Fiber (% Daily Value)    0
Sugars                           0
Protein                          0
Vitamin A (% Daily Value)        0
Vitamin C (% Daily Value)        0
Calcium (% Daily Value)          0
Iron (% Daily Value)             0
dtype: int64
In [8]:
df.duplicated().sum()
Out[8]:
0
In [9]:
df.columns
Out[9]:
Index(['Unnamed: 0', 'Category', 'Item', 'Serving Size', 'Calories',
       'Calories from Fat', 'Total Fat', 'Total Fat (% Daily Value)',
       'Saturated Fat', 'Saturated Fat (% Daily Value)', 'Trans Fat',
       'Cholesterol', 'Cholesterol (% Daily Value)', 'Sodium',
       'Sodium (% Daily Value)', 'Carbohydrates',
       'Carbohydrates (% Daily Value)', 'Dietary Fiber',
       'Dietary Fiber (% Daily Value)', 'Sugars', 'Protein',
       'Vitamin A (% Daily Value)', 'Vitamin C (% Daily Value)',
       'Calcium (% Daily Value)', 'Iron (% Daily Value)'],
      dtype='object')
In [10]:
import matplotlib.pyplot as plt
x=[1,2,6,8,12]
y=[1,28,50,75,100]
plt.plot(x,y,color='blue')
Out[10]:
[<matplotlib.lines.Line2D at 0x236ef4081c0>]
In [11]:
plt.plot(df['Category'],df['Protein'])
plt.xticks(rotation=90)
plt.show()
In [12]:
plt.bar(df['Protein'],df['Vitamin A (% Daily Value)'])
plt.show()
In [13]:
plt.scatter(df['Dietary Fiber'],df['Saturated Fat'])
plt.plot(df['Category'],df['Iron (% Daily Value)'])
plt.xticks(rotation=90)
plt.show()
In [14]:
plt.scatter(df['Category'],df['Sodium'])
plt.show()
In [15]:
plt.bar(df['Dietary Fiber'],df['Total Fat'])
plt.bar(df['Carbohydrates (% Daily Value)'],df['Cholesterol'])
plt.show()
In [16]:
plt.bar(df['Category'],df['Vitamin A (% Daily Value)'],color=['pink','grey','red','blue','yellow','green','black','grey','red','green'])
plt.xticks(rotation=90)
plt.show()
In [17]:
import seaborn as sns
In [18]:
sns.countplot(x='Calcium (% Daily Value)',data=df)
Out[18]:
<AxesSubplot:xlabel='Calcium (% Daily Value)', ylabel='count'>
In [19]:
sns.lineplot(x='Category',y='Sugars',data=df)
Out[19]:
<AxesSubplot:xlabel='Category', ylabel='Sugars'>
In [20]:
sns.violinplot(x='Dietary Fiber',y='Calories from Fat',data=df)
Out[20]:
<AxesSubplot:xlabel='Dietary Fiber', ylabel='Calories from Fat'>
In [21]:
sns.boxplot(x='Vitamin A (% Daily Value)',y='Carbohydrates',data=df)
Out[21]:
<AxesSubplot:xlabel='Vitamin A (% Daily Value)', ylabel='Carbohydrates'>
In [22]:
sns.heatmap(df.corr())
Out[22]:
<AxesSubplot:>
In [23]:
sns.pairplot(df)
Out[23]:
<seaborn.axisgrid.PairGrid at 0x236efb5c3d0>
In [ ]:
df.to_csv('mc donalds.csv')